A direct-sequence or direct sequence coding spread spectrum communication technique in essence combines two digital signals, or bit streams, to create a third signal prior to transmission. The first signal is an information signal, such as the output of a digitized voice circuit. For example, the first signal may have a bit rate of 10 kb/s. The second signal is generated by a random-sequence, or pseudonoise (PN) generator, and is a stream of essentially random bits having a bit rate that is several orders of magnitude greater than the bit rate of the digitized voice signal. The modulation of these two signals results in the third signal having the same bit rate as the second signal. However, the third signal also contains the digitized voice signal. At the receiver, an identical random-sequence generator produces a random bit stream which mirrors the original random-sequence that was used for modulation at the transmitter. For proper operation, after carrier frequency de-modulation, the PN generator of the receiver must be synchronized to the incoming PN sequence. By removing the random sequence from the received signal and integrating it over a symbol period, a despread signal is obtained. Ideally, the despread signal exactly represents the original 10 kb/s voice signal.
A primary function of synchronization in such a spread spectrum communication system is to despread the received pseudonoise (PN) code for demodulation of the received signal. This is accomplished by generating a local replica of the PN code in the receiver, and then synchronizing the local PN signal to the PN signal which is superimposed on the incoming received signal. The process of synchronization is conventionally accomplished in two steps. The first step, referred to as acquisition, brings the two spreading signals into alignment with one another. The second step, referred to as tracking, subsequently and continuously maintains the best possible waveform alignment by means of a feedback loop. Of primary interest herein is the acquisition step of the synchronization process.
Because of the importance of synchronization (or acquisition), many techniques have been proposed which utilize various types of detectors and decision strategies. One common feature of all conventional synchronization techniques that are known to the inventors is that the received signal and the locally generated signal are first correlated to produce a measure of similarity between the two signals. Next, the measure of similarity is compared to a predetermined threshold value to determine if the two signals are in synchronism. If synchronization is detected, then the closed loop tracking system is activated to maintain synchronization. If synchronization is not detected, the acquisition procedure changes a phase of the locally generated PN code and another correlation is attempted as the system searches through the PN phase space.
There are two dwell time (or integration interval) schemes used for correlation: a fixed dwell time and a variable dwell time.
The fixed dwell time approach is relatively simple to implement and analyze and, as a result, finds wide spread use. The fixed dwell time technique can be implemented in one of two ways: as a single dwell time and as a multiple dwell time.
One of the least complex acquisition techniques employs a maximum likelihood approach with a single dwell time. This technique requires that the received PN code signal be correlated with all possible code positions of the local PN code replica. The correlations are performed in parallel, as illustrated in FIG. 1, and the corresponding detector outputs all pertain to the identical observation of the received signal (plus noise). The correct PN alignment is chosen by a comparator for a local PN code phase position which produces the maximum output from the detector. The acquisition can be accomplished rapidly because all possible code offsets are examined simultaneously. However, for long PN codes with a large processing gain, such as those required in spread spectrum systems, the complexity of the parallel implementation is often prohibitive.
The maximum likelihood approach can also be implemented in a serial fashion as illustrated in FIG. 2. Here the received input PN signal is serially correlated with all possible code positions of the local PN code replica and the corresponding correlations are compared with the maximum correlation value obtained from the correlator corresponding to the previous phase of the PN code. At the end of the procedure, the correct PN alignment is chosen so that the local PN code phase position produces the maximum detector (or correlator) output. The maximum likelihood approach uses the maximum detected output over an entire PN space to choose the correct phase of the locally generated PN code for PN alignment. This yields better detection performance by at least 6 dB signal to noise ratio (SNR), in comparison to those systems which utilize the optimal threshold for detection in noisy environments. However, a decision cannot be made until the entire PN code period has been searched. As a result, for long codes with large processing gain, such as those required in Code Division Multiple Access (CDMA) spread spectrum systems, the time to search the entire PN code space before reaching a decision is often prohibitive.
One known synchronization approach that is used in spread spectrum communication systems is referred to as a serial sliding acquisition algorithm. This approach uses a single correlator to serially search for the correct phase of a direct sequence (DS) code signal. More specifically, the serial search is performed by linearly varying the time difference between the PN modulation on the received incoming PN code and the locally generated PN code. A continuous decision process determines when synchronization is achieved. Such a system is also referred to in the literature as a single dwell sliding acquisition system, an example of which is illustrated in FIG. 3.
In that the test for synchronization is based on the crossing of a threshold by the output of the detector, when compared with the serial maximum likelihood acquisition approach discussed above (FIG. 2), the single dwell sliding acquisition system trades off a shorter acquisition time against a reduced accuracy in the detection of synchronization.
In response to this shortcoming an improved version of the single dwell sliding acquisition system was developed to employ multiple correlators (or integration period dwell times). The advantage of the multiple dwell acquisition system is that the examination interval need not be fixed, allowing an incorrect PN phase to be quickly discarded. This results in a shorter search time than is possible using a fixed, single dwell time approach. This type of searching (acquisition) technique is particularly useful for DS code acquisition in a spread spectrum communication system with a large processing gain. The most popular multiple dwell acquisition system is a double dwell acquisition system of a type illustrated in FIG. 4.
The system of FIG. 4 has two integration periods (or dwell times). The first (shorter) dwell time is used to discard incorrect cells quickly and search the correct phase candidate with a rough detection probability, and the second (longer) dwell time is used to provide an improved estimate of whether the in-synch PN code phase has been found. The basic approach thus apportions some false alarm protection in the first integration, and places the remaining (usually greater) false alarm protection in the second integration. In general, the use of the double dwell search approach reduces acquisition time significantly.
The serial sliding search algorithm, whether implemented as a single dwell search (FIG. 3) or as a multiple dwell search (FIG. 4), uses a single threshold (with the single dwell search) or multiple thresholds (with the multiple dwell search) for the determination of the correct PN phase. The best acquisition performance of the serial sliding acquisition system is obtained by using an optimal threshold, or thresholds in the case of a multiple dwell system.
However, in a practical communication environment the optimal threshold is not related to a fixed value, but is instead a function of the signal to noise ratio (SNR). As is well known, and for a communication environment where mobility of the receiver is expected, the SNR of a communication channel will vary as a function of time and as a function of the velocity and location of the receiver.
The optimal threshold level, used to distinguish the signal from the noise level, is always 3 dB lower than the maximum likelihood signal level which is used as the threshold by the maximum likelihood acquisition system. Thus, a single dwell serial acquisition algorithm yields, at best, a detection performance that is 6 dB less than the performance of the maximum likelihood acquisition system.
As a result, in a noisy communication environment the maximum likelihood acquisition system exhibits better acquisition performance by at least 6 dB (in SNR), as compared to the performance of the serial sliding acquisition systems which utilize an optimal threshold for signal detection. However, in the previously described maximum likelihood acquisition system (FIG. 2), a decision cannot be made until the entire code period has been searched. As was noted above, for long PN codes the time to search the entire code space before reaching a decision can be prohibitive.